Avoiding the misuse of BLUP in behavioural ecology

Area of Science:

• Behavioral Ecology
• Quantitative Biology
• Statistical Modeling

Background:

• Individual variation in behavior is key to understanding ecological and evolutionary processes.
• Mixed-effects models are increasingly used to partition behavioral variation within and among individuals.

Purpose of the Study:

• To identify statistical issues arising from using point estimates of individual random effects in subsequent analyses.
• To present robust solutions within the mixed-effects modeling framework for behavioral ecologists.

Main Methods:

• Review of common statistical practices in behavioral ecology.
• Application and demonstration of correct mixed-effects model usage for partitioning behavioral variation.
• Tutorials for implementing recommended statistical solutions.

Main Results:

• Using point estimates of individual random effects introduces significant error, leading to spurious P-values.
• Properly implemented mixed-effects models can accurately account for both among- and within-individual variation.
• Existing statistical frameworks offer robust solutions to these common analytical problems.

Conclusions:

• Behavioral ecologists must be cautious of statistical errors when analyzing individual variation.
• Correct application of mixed-effects models is crucial for reliable insights into behavioral ecology.
• Adopting recommended modeling techniques enhances the validity of research on individual differences.